Technological change in energy - REMIND-MAgPIE: Difference between revisions
m (Mathijs Harmsen moved page Technological change in energy - REMIND to Technological change in energy - REMIND-MAgPIE without leaving a redirect: Text replacement - "REMIND" to "REMIND-MAgPIE") |
m (Text replacement - "REMIND" to "REMIND-MAgPIE") |
||
| Line 1: | Line 1: | ||
{{ModelDocumentationTemplate | {{ModelDocumentationTemplate | ||
|IsEmpty=No | |IsEmpty=No | ||
|IsDocumentationOf=REMIND | |IsDocumentationOf=REMIND-MAgPIE | ||
|DocumentationCategory=Technological change in energy | |DocumentationCategory=Technological change in energy | ||
}} | }} | ||
REMIND assumes endogenous technological change through learning-by-doing for wind and solar power, electric (BEV) and fuel cell vehicle (FCV) technologies, as well as variable renewable energy (VRE) storage, through global learning curves and internalized spillovers. The specific investment costs for wind, solar PV, and solar CSP decrease by 12, 20, and 9%, respectively, for each doubling of cumulated capacity. The capital costs of the generalized storage units for VRE, as well as of advanced vehicle technologies (BEV, FCV), decrease with a 10% learning rate. REMIND reduces learning rates as capacities increase such that the investment costs asymptotically approach endogenously prescribed floor costs. | REMIND-MAgPIE assumes endogenous technological change through learning-by-doing for wind and solar power, electric (BEV) and fuel cell vehicle (FCV) technologies, as well as variable renewable energy (VRE) storage, through global learning curves and internalized spillovers. The specific investment costs for wind, solar PV, and solar CSP decrease by 12, 20, and 9%, respectively, for each doubling of cumulated capacity. The capital costs of the generalized storage units for VRE, as well as of advanced vehicle technologies (BEV, FCV), decrease with a 10% learning rate. REMIND-MAgPIE reduces learning rates as capacities increase such that the investment costs asymptotically approach endogenously prescribed floor costs. | ||
For variable renewable energies, we implemented two parameterized cost markup functions for storage and long-distance transmission grids - see Section "Electricity". To represent the general need for flexibility even in a thermal power system, we included a further flexibility constraint based on Sullivan <ref>Sullivan P, Krey V, Riahi K (2013) Impacts of considering electric sector variability and reliability in the MESSAGE model. Energy Strategy Reviews 1:157–163. doi: 10.1016/j.esr.2013.01.001)</ref>. | For variable renewable energies, we implemented two parameterized cost markup functions for storage and long-distance transmission grids - see Section "Electricity". To represent the general need for flexibility even in a thermal power system, we included a further flexibility constraint based on Sullivan <ref>Sullivan P, Krey V, Riahi K (2013) Impacts of considering electric sector variability and reliability in the MESSAGE model. Energy Strategy Reviews 1:157–163. doi: 10.1016/j.esr.2013.01.001)</ref>. | ||
The techno-economic parameters of power technologies used in the model are given in [http://themasites.pbl.nl/models/advance/index.php/Electricity_-_REMIND Table 2 Techno-economic characteristics of technologies based on exhaustible energy sources and biomass.] for fuel-based technologies and in [http://themasites.pbl.nl/models/advance/index.php/Electricity_-_REMIND Table 3 echno-economic characteristics of technologies based on non-biomass renewable energy sources.] for non-biomass renewables. For wind, solar and hydro, capacity factors depend on grades, see Section "Non-biomass renewables" | The techno-economic parameters of power technologies used in the model are given in [http://themasites.pbl.nl/models/advance/index.php/Electricity_-_REMIND-MAgPIE Table 2 Techno-economic characteristics of technologies based on exhaustible energy sources and biomass.] for fuel-based technologies and in [http://themasites.pbl.nl/models/advance/index.php/Electricity_-_REMIND-MAgPIE Table 3 echno-economic characteristics of technologies based on non-biomass renewable energy sources.] for non-biomass renewables. For wind, solar and hydro, capacity factors depend on grades, see Section "Non-biomass renewables" | ||
As discussed in Section "Macro-economy", REMIND represents energy efficiency improvements via an exogenously prescribed increase in the efficiency parameters of the CES production function, as well as price induced reductions in energy demand and changes in technology choice. | As discussed in Section "Macro-economy", REMIND-MAgPIE represents energy efficiency improvements via an exogenously prescribed increase in the efficiency parameters of the CES production function, as well as price induced reductions in energy demand and changes in technology choice. | ||
REMIND represents investment dynamics in terms of capital motion equations, vintages for energy supply technologies and adjustment costs related to the acceleration of capacity expansion (for further details see Section "Energy conversion"). | REMIND-MAgPIE represents investment dynamics in terms of capital motion equations, vintages for energy supply technologies and adjustment costs related to the acceleration of capacity expansion (for further details see Section "Energy conversion"). | ||
Latest revision as of 14:34, 21 November 2021
| Corresponding documentation | |
|---|---|
| Previous versions | |
| Model information | |
| Model link | |
| Institution | Potsdam Institut für Klimafolgenforschung (PIK), Germany, https://www.pik-potsdam.de. |
| Solution concept | General equilibrium (closed economy)MAgPIE: partial equilibrium model of the agricultural sector; |
| Solution method | OptimizationMAgPIE: cost minimization; |
| Anticipation | |
REMIND-MAgPIE assumes endogenous technological change through learning-by-doing for wind and solar power, electric (BEV) and fuel cell vehicle (FCV) technologies, as well as variable renewable energy (VRE) storage, through global learning curves and internalized spillovers. The specific investment costs for wind, solar PV, and solar CSP decrease by 12, 20, and 9%, respectively, for each doubling of cumulated capacity. The capital costs of the generalized storage units for VRE, as well as of advanced vehicle technologies (BEV, FCV), decrease with a 10% learning rate. REMIND-MAgPIE reduces learning rates as capacities increase such that the investment costs asymptotically approach endogenously prescribed floor costs.
For variable renewable energies, we implemented two parameterized cost markup functions for storage and long-distance transmission grids - see Section "Electricity". To represent the general need for flexibility even in a thermal power system, we included a further flexibility constraint based on Sullivan [1].
The techno-economic parameters of power technologies used in the model are given in Table 2 Techno-economic characteristics of technologies based on exhaustible energy sources and biomass. for fuel-based technologies and in Table 3 echno-economic characteristics of technologies based on non-biomass renewable energy sources. for non-biomass renewables. For wind, solar and hydro, capacity factors depend on grades, see Section "Non-biomass renewables"
As discussed in Section "Macro-economy", REMIND-MAgPIE represents energy efficiency improvements via an exogenously prescribed increase in the efficiency parameters of the CES production function, as well as price induced reductions in energy demand and changes in technology choice. REMIND-MAgPIE represents investment dynamics in terms of capital motion equations, vintages for energy supply technologies and adjustment costs related to the acceleration of capacity expansion (for further details see Section "Energy conversion").
- ↑ Sullivan P, Krey V, Riahi K (2013) Impacts of considering electric sector variability and reliability in the MESSAGE model. Energy Strategy Reviews 1:157–163. doi: 10.1016/j.esr.2013.01.001)